{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exogenous Variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exogenous variables can provide additional information to greatly improve forecasting accuracy. Some examples include price or future promotions variables for demand forecasting, and weather data for electricity load forecast. In this notebook we show an example on how to add different types of exogenous variables to NeuralForecast models for making day-ahead hourly electricity price forecasts (EPF) for France and Belgium markets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All NeuralForecast models are capable of incorporating exogenous variables to model the following conditional predictive distribution:\n",
    "$$\\mathbb{P}(\\mathbf{y}_{t+1:t+H} \\;|\\; \\mathbf{y}_{[:t]},\\; \\mathbf{x}^{(h)}_{[:t]},\\; \\mathbf{x}^{(f)}_{[:t+H]},\\; \\mathbf{x}^{(s)} )$$\n",
    "\n",
    "where the regressors are static exogenous $\\mathbf{x}^{(s)}$, historic exogenous $\\mathbf{x}^{(h)}_{[:t]}$, exogenous available at the time of the prediction $\\mathbf{x}^{(f)}_{[:t+H]}$ and autorregresive features $\\mathbf{y}_{[:t]}$. Depending on the [train loss](https://nixtla.github.io/neuralforecast/losses.pytorch.html), the model outputs can be point forecasts (location estimators) or uncertainty intervals (quantiles)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will show you how to include exogenous variables in the data, specify variables to a model, and produce forecasts using future exogenous variables."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "This Guide assumes basic knowledge on the NeuralForecast library. For a minimal example visit the [Getting Started](./Getting_Started.ipynb) guide.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Exogenous_Variables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `df` dataframe contains the target and exogenous variables past information to train the model. The `unique_id` column identifies the markets, `ds` contains the datestamps, and `y` the electricity price.\n",
    "\n",
    "Include both historic and future temporal variables as columns. In this example, we are adding the system load (`system_load`) as historic data. For future variables, we include a forecast of how much electricity will be produced (`gen_forecast`) and day of week (`week_day`). Both the electricity system demand and offer impact the price significantly, including these variables to the model greatly improve performance, as we demonstrate in Olivares et al. (2022). \n",
    "\n",
    "The distinction between historic and future variables will be made later as parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>system_load</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>53.48</td>\n",
       "      <td>76905.0</td>\n",
       "      <td>74812.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>51.93</td>\n",
       "      <td>75492.0</td>\n",
       "      <td>71469.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>48.76</td>\n",
       "      <td>74394.0</td>\n",
       "      <td>69642.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>42.27</td>\n",
       "      <td>72639.0</td>\n",
       "      <td>66704.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>38.41</td>\n",
       "      <td>69347.0</td>\n",
       "      <td>65051.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds      y  gen_forecast  system_load  week_day\n",
       "0        FR 2015-01-01 00:00:00  53.48       76905.0      74812.0         3\n",
       "1        FR 2015-01-01 01:00:00  51.93       75492.0      71469.0         3\n",
       "2        FR 2015-01-01 02:00:00  48.76       74394.0      69642.0         3\n",
       "3        FR 2015-01-01 03:00:00  42.27       72639.0      66704.0         3\n",
       "4        FR 2015-01-01 04:00:00  38.41       69347.0      65051.0         3"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv')\n",
    "df['ds'] = pd.to_datetime(df['ds'])\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Calendar variables such as day of week, month, and year are very useful to capture long seasonalities.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(df[df['unique_id']=='FR']['ds'], df[df['unique_id']=='FR']['y'])\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price [EUR/MWh]')\n",
    "plt.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the static variables in a separate `static_df` dataframe. In this example, we are using one-hot encoding of the electricity market. The `static_df` must include one observation (row) for each `unique_id` of the `df` dataframe, with the different statics variables as columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>market_0</th>\n",
       "      <th>market_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BR</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id  market_0  market_1\n",
       "0        FR         1         0\n",
       "1        BR         0         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "static_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_static.csv')\n",
    "static_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Training with exogenous variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We distinguish the exogenous variables by whether they reflect static or time-dependent aspects of the modeled data.\n",
    "\n",
    "* **Static exogenous variables**: \n",
    "The static exogenous variables carry time-invariant information for each time series. When the model is built with global parameters to forecast multiple time series, these variables allow sharing information within groups of time series with similar static variable levels. Examples of static variables include designators such as identifiers of regions, groups of products, etc.\n",
    "\n",
    "* **Historic exogenous variables**:\n",
    "This time-dependent exogenous variable is restricted to past observed values. Its predictive power depends on Granger-causality, as its past values can provide significant information about future values of the target variable $\\mathbf{y}$.\n",
    "\n",
    "* **Future exogenous variables**: \n",
    "In contrast with historic exogenous variables, future values are available at the time of the prediction. Examples include calendar variables, weather forecasts, and known events that can cause large spikes and dips such as scheduled promotions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add exogenous variables to the model, first specify the name of each variable from the previous dataframes to the corresponding model hyperparameter during initialization: `futr_exog_list`, `hist_exog_list`, and `stat_exog_list`. We also set `horizon` as 24 to produce the next day hourly forecasts, and set `input_size` to use the last 5 days of data as input.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast.auto import NHITS\n",
    "from neuralforecast.core import NeuralForecast\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "models = [NHITS(h = horizon,\n",
    "                input_size = 5*horizon,\n",
    "                futr_exog_list = ['gen_forecast', 'week_day'], # <- Future exogenous variables\n",
    "                hist_exog_list = ['system_load'], # <- Historical exogenous variables\n",
    "                stat_exog_list = ['market_0', 'market_1'], # <- Static exogenous variables\n",
    "                scaler_type = 'robust')]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "When including exogenous variables always use a scaler by setting the `scaler_type` hyperparameter. The scaler will scale all the temporal features: the target variable `y`, historic and future variables.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Make sure future and historic variables are correctly placed. Defining historic variables as future variables will lead to data leakage.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, pass the datasets to the `df` and `static_df` inputs of the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=models, freq='H')\n",
    "nf.fit(df=df,\n",
    "       static_df=static_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Forecasting with exogenous variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before predicting the prices, we need to gather the future exogenous variables for the day we want to forecast. Define a new dataframe (`futr_df`) with the `unique_id`, `ds`, and future exogenous variables. There is no need to add the target variable `y` and historic variables as they won't be used by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>49118.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>47890.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>47158.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>45991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>45378.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds  gen_forecast  week_day\n",
       "0        FR 2016-11-01 00:00:00       49118.0         1\n",
       "1        FR 2016-11-01 01:00:00       47890.0         1\n",
       "2        FR 2016-11-01 02:00:00       47158.0         1\n",
       "3        FR 2016-11-01 03:00:00       45991.0         1\n",
       "4        FR 2016-11-01 04:00:00       45378.0         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futr_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_futr.csv')\n",
    "futr_df['ds'] = pd.to_datetime(futr_df['ds'])\n",
    "futr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Make sure `futr_df` has informations for the entire forecast horizon. In this example, we are forecasting 24 hours ahead, so `futr_df` must have 24 rows for each time series.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 95.56it/s] \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>NHITS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>36.936493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>33.701057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>30.956253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>28.285088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>27.118006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ds      NHITS\n",
       "unique_id                               \n",
       "BE        2016-11-01 00:00:00  36.936493\n",
       "BE        2016-11-01 01:00:00  33.701057\n",
       "BE        2016-11-01 02:00:00  30.956253\n",
       "BE        2016-11-01 03:00:00  28.285088\n",
       "BE        2016-11-01 04:00:00  27.118006"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_df = df[df['unique_id']=='FR'].tail(24*5).reset_index(drop=True)\n",
    "Y_hat_df = Y_hat_df.reset_index(drop=False)\n",
    "Y_hat_df = Y_hat_df[Y_hat_df['unique_id']=='FR']\n",
    "\n",
    "plot_df = pd.concat([plot_df, Y_hat_df ]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "\n",
    "plot_df[['y', 'NHITS']].plot(linewidth=2)\n",
    "plt.axvline('2016-11-01', color='red')\n",
    "plt.ylabel('Price [EUR/MWh]', fontsize=12)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, to add exogenous variables to a model make sure to follow the next steps:\n",
    "\n",
    "1. Add temporal exogenous variables as columns to the main dataframe (`df`).\n",
    "2. Add static exogenous variables with the `static_df` dataframe.\n",
    "3. Specify the name for each variable in the corresponding model hyperparameter.\n",
    "4. If the model uses future exogenous variables, pass the future dataframe (`futr_df`) to the `predict` method."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, Artur Dubrawski, Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx, International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207022000413)\n",
    "\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
